Deep Understanding of Soccer Match Videos
This work addresses the challenge of automated soccer video analysis for enhancing viewer experience, though it appears incremental as it applies existing computer vision technologies to this domain.
The paper tackles the problem of extracting detailed frame-by-frame information from soccer match videos by developing a system that detects key objects, tracks movements, recognizes player numbers, classifies scenes, and identifies highlights like goal kicks, enabling the generation of highlight GIFs, tactical illustrations, and summary graphs to enrich viewer experience.
Soccer is one of the most popular sport worldwide, with live broadcasts frequently available for major matches. However, extracting detailed, frame-by-frame information on player actions from these videos remains a challenge. Utilizing state-of-the-art computer vision technologies, our system can detect key objects such as soccer balls, players and referees. It also tracks the movements of players and the ball, recognizes player numbers, classifies scenes, and identifies highlights such as goal kicks. By analyzing live TV streams of soccer matches, our system can generate highlight GIFs, tactical illustrations, and diverse summary graphs of ongoing games. Through these visual recognition techniques, we deliver a comprehensive understanding of soccer game videos, enriching the viewer's experience with detailed and insightful analysis.